ARID: A New Dataset for Recognizing Action in the Dark
This addresses the problem of recognizing human actions in low-light conditions for applications like night surveillance and self-driving, but it is incremental as it primarily provides a new dataset.
The paper tackles action recognition in dark videos by introducing the ARID dataset, which includes over 3,780 video clips across 11 action categories, and finds that current models and enhancement methods are ineffective for this task.
The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in the action recognition task for videos in normal illumination, few have studied action recognition in the dark. This is partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset. It consists of over 3,780 video clips with 11 action categories. To the best of our knowledge, it is the first dataset focused on human actions in dark videos. To gain further understandings of our ARID dataset, we analyze the ARID dataset in detail and exhibited its necessity over synthetic dark videos. Additionally, we benchmarked the performance of several current action recognition models on our dataset and explored potential methods for increasing their performances. Our results show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos.